To develop products through a customer-centric strategy, early stages of product development such as target setting play an important role. In the target setting stage Customer Needs (CN) are gathered and translated into Design Requirements (DR) in order to subsequently set product targets that fit cost constraints and at the same time result in high Customer Satisfaction (CS). Continuous advances in information technology create new opportunities for companies to gather information about the customer, for example, for marketing purposes, or to assess customer reactions after the launch of new products. In addition, products are becoming complex systems that are successively equipped with more software and sensors offering opportunities for collecting data on how they are used. Knowing how customers use the product enhances a company’s ability to segment customers and customize products.

Despite customer information availability from different sources (sensors, social media, etc.), surveys and focus groups are considered today as the main data source to derive the set of CN statements during target setting. Further, the team’s interpretation of CNs, which are often described in abstract language, must be translated into DRs, which are described in a more technical language. Hence, the translation process of CNs into DRs is said to be subjective. To set product targets, CS sensitivity to changes in DR levels is also considered. Surveys and benchmarking data containing customer perceptions on competitors’ performance are often the main customer data input into the process. While insightful information may be obtained, surveys are costly and time consuming and only encompass a small part of the market population.

The research presented in this doctoral thesis explores how customer information obtained from sensors (e.g. product usage data) and text data (e.g. from websites, open-survey questionnaires) can be factored in the target setting process before concept generation to enhance customer focus without compromising product development time. The aim is to increase designers’ awareness of target population and in turn increase the quality of the design decisions on product targets. For this purpose, a customer-focused data-driven target setting methodology is proposed. The presented methodology changes the actual target setting methodology by means of indicators and autonomous activities on those parts of the process where marketing or design decisions are needed. The proposed methodology gives the incentive for a more integrated product development where marketing and designers need to work closely. This further allows a sustainable customer information gathering strategy that strives for missing customer information that is required for setting product targets. The indicators act as feedback channels for continuous product improvement. The use of such indicators and autonomous activities highlights the potential of a more efficient, less subjective and higher-quality target setting process.